growth and rates during long-term

natural dissolved organic matter decomposition experiments

by

Eva Teira1, Sandra Martínez-García1, Christian Lønborg2, 3, Xosé A. Álvarez-Salgado2

1Departamento de Ecoloxía e Bioloxía Animal, Universidade de Vigo, 36310 Vigo, Spain

2CSIC, Instituto de Investigacións Mariñas, Eduardo Cabello 6, 36208 Vigo, Spain

3Scottish Association for Marine Science, Oban, Argyll, PA37 1QA, United

1

Abstract

As a first attempt to relate net nitrification rates with bacterial community structure in the coastal embayment of the Ría de Vigo (NW Spain), a set of six long- term (lasting 53-74 days) dissolved organic matter decomposition experiments were conducted in the dark and at a constant temperature of 15ºC with surface seawater collected in January, February, April and June 2008.

Net nitrification rates were estimated from nitrate concentration changes.

Evolution of bacterial community composition was followed using catalyzed reporter deposition-fluorescent in situ hybridization (CARD-FISH) and specific probes for six relevant bacterial groups, including Beta and Gammaproteobacteria.

The growth rates of Betaproteobacteria showed a significant linear positive relationship with nitrification rates that explained 82% of the observed variability, which strongly suggests that the main nitrifying microorganisms during the incubations belonged to the beta subclass of .

A positive relationship was found between net nitrification rates and both ammonium (r2 = 0.92) and phosphate (r2 = 0.97) concentrations, which suggests a tight link among the nitrogen and phosphorus cycles, likely as a result of the role of phosphate as bacterial-growth-limiting nutrient.

2

INTRODUCTION

One of the most challenging goals in ecosystem ecology is to unravel the links between biogeochemical processes and biological community composition. In the sea, the nitrogen cycle depends on the metabolic activities of selected (Karl &

Michaels 2001). Nitrification is the microbial mediated process by which ammonium is oxidized to nitrate. This process influences the partition of N among various pools and may lead to loss of fixed N from the system. Coupled nitrification-denitrification removes up to 50 % of external dissolved inorganic nitrogen inputs to estuaries

(Seitzinger et al. 2006), which reduces the risk of eutrophication in these sensitive areas.

Bacteria contributing to ammonia oxidation, the rate-limiting step of nitrification, belong to the subclass Betaproteobacteria (genera Nitrosomonas and Nitrosospira) and

Gammaproteobacteria (genus ). The second step of nitrification, nitrite- oxidation, is carried out by a variety of belonging to different subclasses of

Proteobacteria (Alpha-, Beta-, and Gamma) and by the genus Nitrospira (Zehr & Ward

2002).

Several groups of Betaproteobacteria able to oxidize ammonia are commonly found in marine and estuarine systems (O`Mullan & Ward 2005). The potential for significant ammonia oxidation by mesophilic Crenarchaea has been also demonstrated in terrestrial, marine and wastewaters treatment environments (see review by Prosser &

Nicol 2008). Growing evidences suggest that Archaea would prevail over bacteria under limiting ammonium and phosphate concentrations, in low pH sulfidic environments, and in low salinity estuarine sediments with high C:N ratios (Mosier & Francis 2008,

Erguder et al. 2009, Martens-Habbena et al. 2009).

3

A dependence of nitrification rates upon ammonium concentration is usually not observed in natural assemblages (Ward 1987, Ward 2002). This lack of correlation has been attributed to very high affinities of ammonia-oxidizers for ammonium or to changes in the ammonia-oxidizing bacterial community composition (Ward 2005).

Rates of nitrification in the Ría de Vigo (NW Spain) obtained from nutrient budgets have been estimated to be remarkably high compared with other coastal upwelling systems, providing about 50% of the nitrate consumed in the photic layer in this upwelling ecosystem (Álvarez-Salgado & Gilcoto 2004). However, the microorganisms involved in nitrification processes have never been studied in this highly productive coastal system. As a first attempt to relate nitrification rates with bacterial community structure in this area, we conducted a set of six long-term (53-74 days) dissolved organic matter decomposition experiments with surface seawater collected from the central segment of the Ría de Vigo under contrasting hydrographic conditions in January, February, April and June 2008. We focused on bacteria as we hypothesized that Archaea likely play a minor role in this marine eutrophic system.

Net nitrification rates were estimated from the net increase of nitrate concentrations during the course of the experiments. Changes in bacterial community composition were followed using catalyzed reporter deposition-fluorescent in situ hybridization (CARD-FISH) and specific probes for six relevant groups, including Beta and Gammaproteobacteria. Despite the limitations of these in vitro field experiments

(reduced turbulence, darkness, and altered grazer communities), nitrification rates have been directly linked to group-specific bacterial growth rates.

4

MATERIAL AND METHODS

Sample collection. Water for the laboratory incubation experiments was collected from a fixed station in the middle segment of the Ría de Vigo (42º14.09’ N,

8º47.18’ W) in winter (31 January, 7 and 14 February 2008), spring (17 and 24 April

2008), and summer (26 June 2008) with an acid-washed 25 litre Niskin bottle at 5 meters depth. Salinity and temperature profiles were recorded prior to water collection with an SBE 9/11 CTD probe.

Experimental design. Filtration of the sample water started within 10 min of collection; one part was filtered through a dual-stage (0.8 µm and 0.2 µm) filter cartridge (Pall-Acropak supor Membrane) which had been pre-washed with 10 litres of

Milli-Q water; the second part was filtered through pre-combusted (450°C for 4 hours)

GF/C filters to collect the bacterial community (< 1.2 m size fraction). After filtration, the water was kept in the dark until arrival in the base laboratory, within 2 hours of collection. The 0.2 filtrate was transferred into a 20 litre carboy and the bacterial community (i.e., the 1.2 m filtrate) was added in a proportion corresponding to 10 % of the total volume. The water was distributed into 24 glass bottles (500 ml), with four replicate bottles being analyzed at days 0, 1, 2, 4, 12 and 53 or 74 (summer experiment only). The glass bottles were kept in the dark at a constant temperature of 15ºC in the six experiments. All glassware was acid washed and rinsed with Milli-Q water prior to use. Samples for the analysis of the dissolved phase were collected from each of the 4 replicate incubation bottles by filtration through 0.2 µm filters (Pall, Supor membrane

+ - - Disc Filter) to follow ammonium (NH4 ), nitrite (NO2 ), nitrate (NO3 ), phosphate

2- (HPO4 ), and (DOC) concentrations. Water samples for nutrient analyses were collected in 50 mL acid washed polyethylene bottles and kept frozen (-20ºC) until analysis. Aliquots for DOC analysis were collected in 10 mL pre- 5 combusted (450ºC, 12 hours) glass ampoules and preserved by adding 50 μL 25 %

H3PO4. Subsamples for bacterial community were collected at days 0, 1, 2, 4, and 53 or

74.

Chemical analyses. DOC samples were measured using a Shimadzu TOC-CSV organic carbon analyzer (Pt–catalyst). To avoid the small error associated with day-to- day instrument variability, all samples from a given experiment were analyzed on a single day. The instrument was checked daily against the deep Sargasso Sea reference water (2,600 m) provided by D.A. Hansell’s laboratory. We obtained an average ± SD concentration of 46.0 ± 2.0 µmol C L–1 while the nominal value provided by the reference laboratory is 44.0 ± 1.5 µmol C L–1.

For the same set of experiments discussed here, Lønborg et al. (2010) adjusted the kinetics of DOC utilization to a first-order exponential decay function using the

Marquardt-Levenberg algorithm and taking the refractory pool into account::

DOC  BDOC·exp k DOC  t  RDOC

where DOC(t) is the concentration of DOC at incubations times 0, 4, 12 and 53-

74 days, BDOC is the bioavailable DOC pool (µmol L-1), kDOC the degradation rate constant (in days–1), t the time (in days) and RDOC the refractory DOC pool (µmol L-1).

RDOC was defined as the concentration at the end of the incubation time (53 or 74 days) and BDOC as the difference between the initial DOC concentration and RDOC.

Since BDOM and RDOM are calculated prior to adjusting the time evolution of

DOC(t), the only parameter that is adjusted with eq. 1 is κDOC.).

Nutrient salts were determined by standard colorimetric methods with and

Alpkem segmented flow analyzer (SFA) (Grasshoff et al. 1983). A 4-point calibration

6 curve of potassium nitrate with concentrations ranging from 0 to 15 μmol L-1 was used to standardize the nitrate+nitrite SFA method, based on the reduction to nitrite in a

Cu/Cd column followed by the determination of nitrite by the standard methods of the azoic salt. Nitrite was measured simultaneously and the concentration of nitrate was obtained by subtracting the nitrite concentration of the sample divided by the yield of the reduction column to the nitrate+nitrite concentration. The accuracy of the method is ± 0.1 μmol L-1 and the curve is linear up to 30 μmol L-1. Net nitrification rates

(in μmol m-3 d-1) were calculated from the accumulation of nitrate during the course of each experiment by subtracting the initial from the final nitrate concentration and dividing by the incubation time.

Bacterial community composition. Fifteen mL were fixed by adding 0.2-m filtered formaldehyde (1-2% final conc.) and subsequently, the samples were stored at

4ºC in the dark for 12-18 h. Thereafter, each sample was filtered through a 0.2 µm polycarbonate filter (Millipore, GTTP, 25 mm filter diameter) supported by a cellulose nitrate filter (Millipore, HAWP, 0.45 µm), washed twice with Milli-Q water, dried and stored in a microfuge vial at -20ºC until further treatment in the laboratory.

Bacterial community composition changes were monitored using Catalyzed

Reporter Deposition-Fluorescence In Situ Hybridization (CARD-FISH) techniques as described in Teira et al. (2008) with a mix of oligonucleotide probes specific for the Eubacteria (EUB338, EUB338II, EUB338III) (Daims et al. 1999), the Beta-

(BET42a) (Manz et al. 1992) and Gammaproteobacteria (GAM42a) (Manz et al. 1992) subclasses and the class Flavobacteria of phylum (CF319a) (Manz et al.,

1996). Additionally, the relative abundance of the SAR11, Roseobacter, and SAR86 clusters was also analyzed using the specific probes SAR11-441R (Morris et al. 2002),

7

Ros537 (Eilers et al. 2001), and SAR86/1245 (Zubkov et al. 2001). The Eub antisense probe Non338 probe was used as negative control.

Filters for CARD-FISH were embedded in low-gelling-point agarose and incubated with lysozyme. Filters were cut in sections and hybridized at 35 ºC with horseradish peroxidase (HRP)-labelled oligonucleotide probes during 4-12 hours.

Tyramide-Alexa488 was used for signal amplification (30-40 minutes). We used 55% of formamide for all probes excepting for SAR11-441R (45 % formamide). Cells were counter-stained with a DAPI-mix (5.5 parts of Citifluor [Citifluor, Ltd.], 1 part of

Vectashield [Vector Laboratories, Inc.] and 0.5 parts of PBS with DAPI (final concentration 1 µg mL-1).

The slides were examined under a Leica DMBL microscope equipped with a

100-W Hg-lamp and appropriate filter sets for DAPI and Alexa488. More than 800

DAPI-stained cells were counted per sample. For each microscope field, 2 different categories were enumerated: (i) total DAPI-stained cells, (ii) cells stained with the specific probe. Negative control counts (hybridization with HRP-Non338) averaged

0.5% of DAPI-stained cells. The counting error, expressed as the percentage of standard error between replicates, was, on average, < 5% for DAPI counts and < 10% for FISH counts. The counting error was relatively higher (33 %) for the less abundant groups

(Betaproteobacteria and SAR86).

Group-specific bacterial net growth rates over the full incubation period were estimated using the logistic equation as follows:

N = Kn / (1 + exp (a – μ·t))

Where N represents cell abundance (cells mL-1), Kn is the cell abundance at the end of the experiment (day 53 or 74), a is a constant parameter, t is the time (d) and  is 8 the growth rate (d-1). A Student T-test was used to assess if the derived growth rates were significantly higher than zero.

RESULTS AND DISCUSSION

The percentage of detected bacteria (EUB-positive cells) slightly increased during the incubation in January and February. By contrast, during the experiments conducted in April and June, the percentage of detected bacteria significantly decreased

(Fig. 1). The set of six FISH probes used accounted on average for 85% and 80% of total detected bacteria at the beginning and at the end of the experiments, respectively.

The initial bacterial community was dominated by the group Bacteroidetes except in January, when the group SAR11 was the most abundant (Fig. 1). The dominance of Bacteroidetes has been widely reported in productive marine pelagic systems (Riemann et al. 2000, Alderkamp et al. 2006, Teira et al. 2008). The dominance of SAR11 in January was related to the entry of DOC-poor Eastern North Atlantic

Central Water (ENACW) into the Ría de Vigo associated to the upwelling-downwelling dynamics of this embayment (Teira et al. 2009). The relative abundance of Beta and

Gammaproteobacteria, which include several , was low at the beginning of the experiments (on average 2 and 4 % of DAPI-counts, respectively).

Betaproteobacteria significantly increased their relative abundances during the incubation time in all the experiments (t-test, p<0.045), accounting for up to 23 % of

DAPI-counts at the end of the experiment conducted in January. The group

Gammaproteobacteria showed a significantly higher relative abundance at the end than at the beginning of the experiment conducted in January (t-test, p=0.012). All the other

9 groups either remained constant (t-test, p>0.05) or showed a significant decrease in their relative abundances (t-test, p<0.05).

In most of the cases, the concentration of nitrate does not change too much over the first 12 days of incubation; it accumulates between days 12 and 50 (Fig. 2).

Exceptions to this trend are experiments 1 and 2 (a linear increase is observed) and 5 (a logarithmic increase is observed). For this reason, we decided to calculate the net nitrification rate just by subtracting the initial from the final nitrate concentration and dividing by the incubation time. Net nitrification rates ranged from 6 to 103 mol NO3 m-3 d-1, being higher in winter than in spring and summer. Although the net nitrification rates derived from our incubations are within the range of previous estimates by

Álvarez-Salgado & Gilcoto (2004) for the same region, the highest rate was one order

-3 -1 of magnitude lower than the highest nitrification rate (1400 mol NO3 m d ) calculated by these authors under natural field conditions. It is important to note the significant methodological differences between the present study (10% dilution bacterial cultures, long incubations, dark conditions, etc) and that by Álvarez-Salgado &

Gilcoto (2004) (in situ nutrient budgets), which preclude us to directly compare the magnitude of nitrification during both studies. Nevertheless, the measured nitrification rates agree very well with those rates of ammonium oxidation measured by Ward

(2005) over a 2-yr study in Monterey Bay (<20 to ca 80 mol m-3 d-1) using the 15N trace method during 3 to 6-h incubations under simulated in situ conditions.

Betaproteobacteria net growth over the whole incubation period (53 or 74 days) rates was calculated from the time-course of their absolute abundances in the six experiments using the logistic equation (Fig. 3). All the net growth estimates were significantly higher than zero except that of the 24-Apr experiment, and ranged from 0.4

10 to1.4 d-1 (Table 2). The determination coefficient of the adjusted models ranged from

0.85 to 0.99. Higher net growth rates were measured in winter than in spring and summer. The net growth rate of Gammaproteobacteria was significantly different from zero only in the experiment conducted in January (0.46 d-1). As mentioned before, the abundance of all the other groups remained stable or decreased during the incubations, showing a positive net growth only for 1-2 days.

The net growth rates of Betaproteobacteria showed a significant linear positive relationship with the net nitrification rates (Fig. 4A). Furthermore, the net growth of this bacterial group explained 82% of the variability observed in net nitrification rates, which strongly suggests that the main nitrifying microorganisms during our incubations belong to the beta subclass of Proteobacteria. Although both Beta- and

Gammaproteobacteria include ammonia oxidizing bacteria (AOB), which carry out the limiting step in nitrification, it has been observed that Gammaproteobacteria AOB are not very abundant in seawater (O’Mullan and Ward. 2005). Nonetheless, Lam et al

(2007) found that despite their low abundance, ammonia oxidizing

Gammaproteobacteria can significantly contribute to nitrification in the Black Sea suboxic zone.

It has been recently reported the potential major role of Archaea as ammonia oxidizing microorganisms in several environments. From the available information on the potential niches of ammonia oxidizing Archaea (AOA) and AOB, we presumed that

Archaea do not play a relevant role in nitrification in this eutrophic pelagic ecosystem

(Mosier & Francis 2008, Erguder et al. 2009, Di et al. 2009, Martens-Habbena et al.

2009). Assuming that CARD-FISH detection rates are equal or even lower at day 53 or

74 than at day 0, the fact that, in the winter experiments (31-Jan, 7_Feb, and 14-Feb), the initial contribution of bacteria to total prokaryotic abundance (52, 55 and 53%, 11 respectively) did not significantly decrease from its contribution at the end of the incubations (68, 54, and 67%, respectively) (T-test, p > 0.16), suggests that Archaea did not considerably grow in our experimental bottles. By contrast, the relative abundance of bacteria did significantly decrease (T-test, p<0.05) at the end of the incubation in the experiments conducted in spring and summer (from 95, 95, and 82% at the beginning to

52, 51 and 49% at the end in 17-Apr, 24-Apr and 26-Jun, respectively), which might indicate an increase in archaeal abundance. Unfortunately we do not have information about the abundance and the community composition of Archaea in our experiments, which preclude us to speculate about their potential role as nitrifiers in the Ría de Vigo.

The derived maximum net growth rate for Betaproteobacteria is within the range of reported rates for ammonia (0.48-2.11 d-1) and nitrite-oxidizing bacteria (0.43-1.39 d-1) (Prosser 2007), and are also similar to maximum growth rates of the ammonia oxidizing Archaea (AOA) “Candidatus Nitrosopumilus maritimus” strain SCM1

(Martens-Habbena et al. 2009). AOB are autotrophic bacteria that use ammonium as their sole source of energy, and carbon dioxide as carbon source, which results in a rather inefficient growth. Therefore, it is generally accepted that the growth rate of nitrifying bacteria is significantly lower than that of heterotrophic bacteria. This can be certainly true under culture conditions. However, the growth rates of nitrifying bacteria reported in natural conditions are not remarkably lower than the range of growth rates reported for heterotrophic bacteria in marine waters (Yokokawa et al. 2004, Teira et al.

2009).

It is possible that not all of the Betaproteobacteria detected with CARD-FISH are nitrifiers and thus, autotrophic; however, the high positive correlation with nitrification and the negative marginally significant (p = 0.08) correlation with DOC degradation rates (Fig. 4B), strongly suggest that a significant fraction of the detected 12

Betaproteobacteria during the incubations were autotrophic. The fact that the

Betaproteobacteria growth is lower as the DOC degradation rates are higher could reflect a direct competition of autrotrophic and heterotrophic bacteria for the available ammonium. It has been reported that autotrophic ammonia oxidation can be either depressed (Verhagen et al. 1992) or enhanced (Clark and Schmidt 1966) by heterotrophic bacteria. A recent study by Taylor and Townsend (2010) suggests that heterotrophic bacteria would outcompete nitrifying bacteria for ammonium uptake under a situation of nitrogen limitation (high DOC:nitrate ratios). The highest

DOC:nitrate ratios were found in the spring and summer experiments (Table 1). Thus, the low nitrification rates measured in spring and summer could result from heterotrophic bacteria outcompeting nitrifying bacteria.

Provided that ammonia oxidation is the limiting step in nitrification both AOB growth rates and nitrification rates are expected to be directly linked to the availability of ammonium. We actually found a strong positive relationship (r2 = 0.92) among the initial ammonium concentration and the nitrification rates (Fig. 5A). However, the relationship was not linear, and fitted better to a positive exponential model. Figure 4A suggests that at low ammonium concentration (<2 mol L-1) other factors were ultimately controlling nitrification rates. Most studies on environmental factors controlling nitrification in marine pelagic systems point to temperature, light and oxygen concentrations, besides ammonium availability, as the main regulatory factors

(de Bie et al. 2002, Andersson et al. 2006, Sugimoto et al. 2009). Regardless of the very limited range of variability in water temperature and initial oxygen concentration (Table

1) covered during our sampling period, the estimated nitrification rates varied by more than one order of magnitude (Table 2). In addition, all the incubations were conducted

13 in the dark and at a constant temperature (15 ºC), suggesting that the aforementioned factors must play a minor role explaining the variability in our dataset.

We explored the relationship of nitrification rates and other environmental variables such as salinity, DOC concentration or phosphate concentration. We found that phosphate concentration explained 97 % of the variability in nitrification rates (Fig.

5B). Actually, nitrification rates appear to be better explained by phosphate concentration when nutrients are in short supply. There is not much information about the effect of phosphate deficiency on nitrification. Hefort et al. (2007) also demonstrated a positive correlation between crenarchaeotal amoA gene copies and phosphate concentration in the southern North Sea. A positive correlation between nitrification or amoA gene copies and phosphate can merely reflect the role of phosphate as a bacterial growth-limiting nutrient (see review in Church 2008). . The fact that during spring and summer, when the phosphate concentration is lower than in winter, the nitrification rates are low and the DOC degradation rates are high, suggests that a severe P deficiency may limit even more the ability of nitrifying bacteria to compete for N with heterotrophic bacteria. Purchase (1974) demonstrated the influence of phosphate deficiency on nitrification and concluded that nitrite oxidizers are more sensitive to P deficiency than ammonia oxidizers, which can severely affect their ability to compete for nitrogen, ultimately limiting nitrification. Our results thus demonstrate that nitrification depends not only on the availability of ammonia but also on the availability of phosphate.

Conclusions. Betaproteobacteria growth explained 82% of the observed variability in nitrification rates in the coastal eutrophic system of the Ría de Vigo; which highlights the role of members of this group as nitrifiers in this system.

14

Nevertheless, further research is needed to simultaneously assess the potential role of both bacteria and Archaea.s.

Ammonium concentration explained 92% of the variability observed in nitrification rates in the Ría de Vigo, although phosphate appears to be also an important controlling factor, which implies a tight link among the nitrogen and phosphorous cycles.

Acknowledgments. This study was funded by a fellowship to CL from the Early Stage

Training site ECOSUMMER (MEST-CT-2004-0205019. We thank the captain, crew and technicians of R/V Mytilus and the members of the Department of Oceanography of the Instituto de Investigacións Mariñas for the collaboration during the sampling program. Access to vessel time and meteorological and hydrological data were provided by the RAFTING project (CTM2007-61983/MAR). SM-G was founded by a FPU-MEC fellowship. E.T. was founded by a Ramón y Cajal-MEC contract.

15

LITERATURE CITED

Alderkamp A-C, Sintes E, Herndl GJ (2006) Abundance and activity of major groups of

prokaryotic plankton in the coastal North Sea during spring and summer. Aquat

Microb Ecol 45: 237-246

Álvarez-Salgado XA, Gilcoto M (2004) Inferring nitrification rates with an inverse

method in a coastal upwelling system, Ría de Vigo (NW Spain). Mar Ecol Prog

Ser 276: 3-17

Andersson MGI, Brion N, Middelburg JJ (2006) Comparison of nitrifier activity versus

growth in the Scheldt Estuary- a turbid, tidal estuary in northern Europe. Aquat

Microb Ecol 42: 149-158

Church MJ (2008) Resource control of bacterial dynamics in the sea. In: Kirchman DL

(ed) Microbial Ecology of the Oceans (2nd edition). Wiley-Liss, New York, pp:

335–382

Clark C, Schmidt EL (1966) Effect of mixed culture on Nitrosomonas europaea

simulated by uptake and utilization of pyruvate. J Bacteriol 91: 367-373

Daims H, Bruhl A, Amann R, Schleifer KH, Wagner M (1999) The domain-specific

probe EUB338 is insufficient for the detection of all Bacteria: Development and

evaluation of a more comprehensive probe set. Syst Appl Microbiol 22:434-444.

De Bie MJM, Starink M, Boschker HTS, Peene JJ, Laanbroek HJ (2002) Nitrification in

the Schelde estuary: methodological aspects and factors influencing its activity.

FEMS Microbiol Ecol 42:099-107

16

Di HJ, Cameron KC, Shen JP, Winefield CS, O’Callaghan M, Bowatte S, He JZ (2009)

Nitrification driven by bacteria and not archaea in nitrogen-rich grassland soils.

Nature Geoscience doi: 10.1038/NGEO613

Eilers H, Pernthaler J, Peplies J, Glöckner FO, Gerdts G, Amann R (2001) Isolation of

novel pelagic bacteria from the German Bight and their seasonal contributions to

surface picoplankton. Appl Environ Microbiol 67: 5134-5142

Erguder TH, Boon N, Wittebolle L, Marzorati M, Verstraete W (2009) Environmental

factors shaping the ecological niches of ammonia-oxidizing archaea. FEMS

Microbiol Rev 33: 855-869

Grasshoff K, Ehrhardt M, Kremling M (1983) Methods of seawater analysis, 2nd edn.

Verlag Chemie, Weinheim, 419 pp

Hefort L, Schouten S, Abbas B, Veldhuis MJW, Coolen MJL, Wuchter C, Boon JP,

Herndl GJ, Sinninghe Damsté JS (2007) Variations in spatial and temporal

distribution of Archaea in the North Sea in relation to environmental variables.

FEMS Microbiol Ecol 62: 242-257

Karl DM, Michaels AF (2001) Nitrogen Cycle. Academic Press

Lam P, Jensen MM, Lavik G, McGinnis DF, Muller B, Schubert CJ, Amann R,

Thamdrup B, Kuypers MMM (2007) Linking crenarchaeal and bacterial

nitrification to anammox in the Black Sea. Proc Natl Acad Sci USA 104: 7104-

7109

Lønborg C, Álvarez-Salgado XA, Martínez-García S, Miller AEJ, Teira E (2010)

Stoichiometry of dissolved organic matter and the kinetics of its microbial

degradation in a coastal upwelling system. Aquat Microb Ecol 58: 117-126

17

Manz W, Amann R, Ludwig W, Wagner M, Schleifer KH (1992) Phylogenetic

oligodeoxynucleotide probes for the major subclasses of proteobacteria-

problems and solutions. Syst Appl Microbiol 15: 593-600

Manz W, Amann R, Ludwig W, Vancanneyt M, Schleifer KH (1996) Application of a

suite of 16S rRNA-specific oligonucleotide probes designed to investigate

bacteria of the phylum cytophaga-flavobacter-bacteroides in the natural

environment. Microbiology-UK 142: 1097-1106

Martens-Habbena W, Berube PM, Urakawa H, de la Torre JR, Stahl DA (2009)

Ammonia oxidation kinetics determines niche separation of nitrifying Archaea

and Bacteria. Nature, doi: 10.1038/nature08465

Morris RM, Rappé MS, Connon SA, Vergin KL, Slebold WA, Carlson CA, Giovanonni

SJ (2002) SAR11 clade dominates ocean surface bacterioplankton communities.

Nature 420: 806-810

Mosier AC, Francis CA (2008) Relative abundance and diversity of ammonia-oxidizing

archaea and bacteria in the San Francisco Bay estuary. Environ Microbiol 10:

3002-3016

O’Mullan GD, Ward BB (2005) Relationship of temporal and spatial variabilities of

ammonia-oxidizing bacteria to nitrification rates in Monterey Bay, California.

Appl Environ Microbiol 71: 697-705

Prosser JI (2007) The ecology of nitrifying bacteria. In: Bothe H, Fergunson SJ, Newton

WE (eds) Biology of the nitrogen cycle. Elsevier, p 223-243

Prosser JI, Nicol GW (2008) Relative contributions of archaea and bacteria to aerobic

ammonia oxidation in the environment. Environ Microbiol 10: 2931-2941

18

Purchase BS (1974) The influence of phosphate deficiency on nitrification. Plant and

soil 41: 541-547

Riemann L, Steward GF, Azam F (2000) Dynamics of bacterial community

composition and activity during a mesocosm diatom bloom. Appl Environ

Microbiol 66: 578-587

Seitzinger S, Harrison JA, Bohlke JK, Bouwman AF, Lowrance R, Peterson B and

others (2006) Denitrification across landscapes and waterscapes: a synthesis.

Ecol Appl 16: 2064-2090

Sugimoto R, Kasai A, Miyajima T, Fujita K (2009) Controlling factors of seasonal

variation in the nitrogen isotope ratio of nitrate in a eutrophic coastal

environment. Est Coast Shelf Sci 85: 231-240

Taylor PG, Townsend AR (2010) Stoichiometric control of organic carbon-nitrate

relationships from soils to the sea. Nature 464: 1178-1181

Teira E, Gasol JM, Aranguren-Gassis M, Fernández A, González J, Lekunberri I,

Álvarez-Salgado XA (2008) Linkages between bacterioplankton community

composition, heterotrophic carbon cycling and environmental conditions in a

highly dynamic coastal ecosystem. Environ Microbiol 10: 906-917

Teira E, Martínez-García S, Lønborg C, Álvarez-Salgado XA (2009) Growth rates of

different phylogenetic bacterioplankton groups in a coastal upwelling system.

Environmental Microbiology Reports 1: 545-554

Verhagen FJM, Duyts H, Laanbroek HJ (1992) Competition for ammonium between

nitrifying and heterotrophic bacteria in continuously percolated soil columns.

Appl Environ Microbiol 58: 3303-3311

19

Ward BB (1987) Nitrogen transformations in the Southern California Bight. Deep-Sea

Res 34: 785-805

Ward BB (2002) Nitrification in aquatic systems. In: Capone DG (ed) Encyclopedia of

Environmental Microbiology. John Wiley and Sons, New York, p 2144-2167

Ward BB (2005) Temporal variability in nitrification rates and related biogeochemical

factors in Monterey Bay, California, USA. Mar Ecol Prog Ser 292: 97-109

Ward BB, Eveillard D, Kirshtein JD, Nelson JD, Voytek MA, Jackson GA (2007)

Ammonia-oxidizing bacterial community composition in estuarine and oceanic

environments assessed using a functional gene microarray. Environ Microbiol

doi: 10.1111/j.1462-2920.2007.01371.x

Yokokawa T, Nagata T, Cottrell MT, Kirchman DL (2004) Growth rate of the major

phylogenetic bacterial groups in the Delaware estuary. Limnol Oceanogr 49:

1620-1629

Zehr JP, Ward BB (2002) Nitrogen cycling in the ocean: new perspectives on processes

and paradigms. Appl Environ Microbiol 68: 1015-1024

Zubkov MV, Fuchs BM, Burkill PH, Amann R (2001) Comparison of cellular and

biomass specific activities of dominant bacterioplankton groups in stratified

waters of the Celtic sea. Appl Environ Microbiol 67: 5210-5216

20

Figure legends

Figure 1. Composition of the bacterial assemblage at the beginning (Day 0) and at the end (Day 53 or 74) of the six incubation experiments. The relative abundance of each group (ROS, Roseobacter; SAR11; BETA, Betaproteobacteria; CFB, Bacteroidetes;

SAR86; GAMMA, Gammaproteobacteria) is expressed as percentage of total DAPI- stained cells. Dashed black line represents the percentage of prokaryotes detected with the mix of oligonucleotide probes specific for the domain Eubacteria (EUB). Error bars represent standard errors.

Figure 2. Time course of nitrate (black diamonds) and ammonium (white diamonds) concentration (mol L-1) during the six dilution experiments. The error bars represent the standard deviation from four replicates. Note the different scales for the 17-Apr, 24-

Apr and 26-Jun experiments.

Figure 3. Time course of prokaryotic abundance (DAPI-stained cells) (black diamonds) and Betaproteobacteria abundance (white diamonds) during the six dilution experiments. The error bars represent the standard error from two replicates. For clarity, the adjusted logistic growth curve (black line) for Betaproteobacteria has been drawn only between day 0 and 4

Figure 4. Plots showing the relationship between Betaproteobacteria growth and nitrification rates (y = 0.009 * x +0.37) (A) or dissolved organic carbon (DOC) degradation rates (KDOC) (y = -3.8 * x +1.51) (B). Errors bars represent standard error from two (Betaproteobacteria growth) or four (nitrification and DOC degradation rates) replicates. Data point from 24-Apr was excluded due to no significant growth rate (see

Table 2)

21

Figure 5. Plots showing the relationship between nitrification rates and ammonium (A) or phosphate concentration at the beginning of the experiments (B). Error bars represent standard error from four replicates.

22

Table 1. Environmental conditions in the Ría de Vigo at the sampling site (5 m depth)

on the water collection dates. Salinity (Sal), temperature (Temp), dissolved oxygen

(DO), chlorophyll a (Chl a), and dissolved organic carbon (DOC).

2- - + Temp Chla HPO4 NO3 NH4 DOC Date Sal (ºC) (μg L-1)(μmol L-1) (μmol L-1) (μmol L-1) (μmol L-1)

31-01-2008 35.0 13.0 1.52 0.46 ± 0.01 6.9 ± 0.1 2.2 ± 0.1 75 ± 1

07-02-2008 34.5 13.1 0.81 0.56 ± 0.01 6.9 ± 0.1 3.2 ± 0.1 77 ± 1

14-02-2008 35.2 13.4 1.13 0.42 ± 0.01 5.8 ± 0.1 2.3 ± 0.1 73 ± 1

17-04-2008 34.8 14.3 3.04 0.09 ± 0.02 0.1 ± 0.1 0.9 ± 0.7 81 ± 1

24-04-2008 25.0 15.5 8.42 0.02 ± 0.01 3.9 ± 0.1 1.8 ± 0.6 85 ± 1

26-06-2008 35.1 17.4 4.32 0.27 ± 0.01 0.2 ± 0.1 0.8 ± 0.1 88 ± 1

23

Table 2. Betaproteobacteria (Beta) growth rates, nitrification rates and dissolved organic carbon degradation rate (KDOC) (±SE) estimated for the six experiments. KDOC values were taken from Lønborg bet al. (2010). NS, not significantly different from zero.

Beta growth Nitrification KDOC

-1 -3 -1 -1 Date d mol NO3 m d d

31-01-2008 1.1±0.2 51 ± 7 0.11 ± 0.03

07-02-2008 1.2±0.1 103 ± 5 0.11 ± 0.01

14-02-2008 0.9±0.2 59 ± 4 0.20 ± 0.01

17-04-2008 0.4±0.1 8 ± 1 0.20 ± 0.02

24-04-2008 0.5±0.4 (NS) 6 ± 4 0.20 ± 0.02

26-06-2008 0.5±0.1 24 ± 2 0.30 ± 0.08

24

ROS SAR11 Day 0 100 BETA CFB SAR86 80 GAMMA % EUB 60 %DAPI 40

20

0 31-Jan 7-Feb 14-Feb 17-Apr 24-Apr 26-Jun Experiment date

ROS Day 53 or 74 SAR11 100 BETA CFB SAR86 80 GAMMA % EUB 60 %DAPI 40

20

0 31-Jan 7-Feb 14-Feb 17-Apr 24-Apr 26-Jun Experiment date

Figure 1

25

) ) -1

14 -1 5 12 31-Jan 17-Apr 4 mol L 10 mol L   ( 8 3 + ( 4 + 6 4 2 4 1 or NH

- 2 or NH 3 - 0 3 0

NO 0 4 8 12505560657075 0 2 4 6 8 1012 50 55 60 65 70 75 NO ) Day ) Day -1 14 -1 5 24-Apr 12 07-Feb 4 mol L 10 mol L   ( ( 3 + 8 + 4 4 6 2 4 1 or NH or NH - 2 - 3 3 0 0 NO 0 2 4 6 8 1012 50 55 60 65 70 75 NO 0 2 4 6 8 1012 50 55 60 65 70 75 ) Day ) Day -1 14 -1 5 12 14-Feb 26-Jun

mol L 4 mol L  10  ( ( + 8 + 3 4 4 6 2 4 or NH or NH 1 - -

3 2 3 0 0 NO 0 2 4 6 8 1012 50 55 60 65 70 75 NO 0 2 4 6 8 1012 50 55 60 65 70 75 Day Day

- NO3  

Figure 2

26

) )

-1 16 -1 16 14 14 12 12 10 10 8 8 31-Jan 6 6 17-Apr

0 1 2 3 4 5 50 55 60 65 70 75 0 1 2 3 4 5 50 55 60 65 70 75 Ln Abundance (cel mL Ln Abundance (cel mL Ln Abundance (cel

) Day ) Day -1 16 -1 16 14 14 12 12 10 10 8 8 07-Feb 6 6 24-Apr

0 1 2 3 4 5 50 55 60 65 70 75 0 1 2 3 4 5 50 55 60 65 70 75 Ln Abundance (cel mL (cel Ln Abundance mL (cel Ln Abundance

) Day ) Day -1 16 -1 16 14 14 12 12 10 10 8 8 14-Feb 6 6 26-Jun

0 1 2 3 4 5 50 55 60 65 70 75 0 1 2 3 4 5 50 55 60 65 70 75 Ln Abundance (cel mL Ln Abundance (cel mL Ln Abundance (cel Day Day Prokaryotes -proteobacteria

Figure 3

27

) 1.6 1.6 -1 r2 = 0.82, p = 0.034 r2 = 0.68, p = 0.08

1.2 1.2

0.8 0.8

0.4 0.4

A B -proteobacteria growth (d growth -proteobacteria

 0.0 0.0 0 20 40 60 80 100 120 0.0 0.1 0.2 0.3 0.4 - -3 -1 -1 Nitrification (mol NO m d ) KDOC (d ) 3

Figure 4

28

) )

-1 120 -1 120

d 2 d

-3 r = 0.92, p = 0.024 -3 2 100 100 r = 0.97, p<0.005 m m - - 3 3 80 80

60 60 mol NO mol NO mol   40 40

20 20 A B 0 0 Nitrification ( Nitrification ( 0123 0,0 0,1 0,2 0,3 0,4 0,5 0,6

-1 -1 Ammonium (mol L ) Phosphate (mol L )

Figure 5

29